The transmission of digital information is subject to the adverse effects of the communication channel, such as co-channel and adjacent channel interference, noise, dispersion, and fading. These effects introduce bit errors into the transmitted data stream. The number of errors in a received data frame is sometimes measured as Bit Error Rate (BER) and Frame Erasure/Error Rate (FER). BER is defined as the ratio of the number of bits received in error to the total number of received bits. BER reflects the quality of a received data frame or block, which is useful in transport format selection, transmission rate selection, power control, link quality estimation, etc.
Besides BER, Frame Qualities (FQ) can be measured or estimated by the receiver for Bad Frame Indication (BFI), Adaptive Multi-Rate (AMR), Link Quality (LQ) measurement, or Bit Error Probability (BEP) computation. BFI indicates whether a decoded frame can be regarded as a bad frame that contains too many errors to be played by speech decoder. BFI usually is derived from a Soft Frame Quality (SFQ) estimate, which is often generated by a SFQ estimation module in a receiver. An SFQ estimate reflects the number of bits corrected by a decoder in a decoded data frame. If the number of corrected bits is higher than a threshold, the data frame is considered a bad frame and the binary BFI flag may be set to indicate to a higher layer whether this data frame can be used or not. For example, BFI=0 may indicate that the data frame contains meaningful information bits while BFI=1 may indicate that the number of corrected bits in the data frame exceeds the threshold. The data frame is not necessarily discarded because, in such condition, the corrections identified by the decoder may be incorrect.
The SFQ values generated by a SFQ estimation module can be used for many purposes. However, in prior art SFQ estimation procedures, an encoding process is needed to re-encode the decoded hard-bits. The re-encoded data is then compared with the soft bits that are output from the demodulator. Both the re-encoding process and the comparison process require extra computational power and additional memory resources, resulting in higher power consumption and unnecessary processing delays.
Further, prior art SFQ estimation modules produce a frame quality that is related to the actual soft bit frame quality in a complicated way. The relationship between the produced frame quality and the actual soft bit frame quality changes depending on the Signal to Noise Ratio (SNR) level, received power level, channel type or encoding rate, channel conditions, etc.
There is a need for simpler, faster, and more efficient SFQ estimation.